Internet of things (IoT) has become one of the eminent phenomena in human life along with its collaboration with wireless sensor networks (WSNs), due to enormous growth in the domain; there has been a demand to address the various issues regarding it such as energy consumption, redundancy, and overhead. Data aggregation (DA) is considered as the basic mechanism to minimize the energy efficiency and communication overhead; however, security plays an important role where node security is essential due to the volatile nature of WSN. Thus, we design and develop proximate node aware secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional data is used to secure the original data, and further information is shared with the proximate node; moreover, further security is achieved by updating the state each time. Moreover, the node that does not have updated information is considered as the compromised node and discarded. PNA-SDA is evaluated considering the different parameters like average energy consumption, and average deceased node; also, comparative analysis is carried out with the existing model in terms of throughput and correct packet identification.

1. International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 13, No. 1, March 2024, pp. 143~150
ISSN: 2089-4864, DOI: 10.11591/ijres.v13.i1.pp143-150  143
Journal homepage: http://ijres.iaescore.com
Proximate node aware optimal and secure data aggregation in
wireless sensor network based IoT environment
Sushma Priyadarshini1
, Asma Parveen2
1
Department of Computer Science and Engineering, Kallerawan Charitable Trust Engineering College, Gulbarga, India
2
Department of Applied Sciences, Faculty of Engineering and Technology, Khaja Bandanawaz University, Gulbarga, India
Article Info ABSTRACT
Article history:
Received Jul 26, 2022
Revised Apr 20, 2023
Accepted May 11, 2023
Internet of things (IoT) has become one of the eminent phenomena in human
life along with its collaboration with wireless sensor networks (WSNs), due
to enormous growth in the domain; there has been a demand to address the
various issues regarding it such as energy consumption, redundancy, and
overhead. Data aggregation (DA) is considered as the basic mechanism to
minimize the energy efficiency and communication overhead; however,
security plays an important role where node security is essential due to the
volatile nature of WSN. Thus, we design and develop proximate node aware
secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional
data is used to secure the original data, and further information is shared with
the proximate node; moreover, further security is achieved by updating the
state each time. Moreover, the node that does not have updated information is
considered as the compromised node and discarded. PNA-SDA is evaluated
considering the different parameters like average energy consumption, and
average deceased node; also, comparative analysis is carried out with the
existing model in terms of throughput and correct packet identification.
Keywords:
Data aggregation
Internet of things
Proximate node aware-SDA
Secure data aggregation
Wireless sensor network
This is an open access article under the CC BY-SA license.
Corresponding Author:
Sushma Priyadarshini
Department of Computer Science and Engineering, Kallerawan Charitable Trust Engineering College
Gulbarga, Karnataka, India
Email: sushmapriyadarshini12@gmail.com
1. INTRODUCTION
Wireless sensor network, also popularly known as WSN comprises some sensors. One can define it
as a wireless network that is self-configured and it does not have and specific infrastructure. The purpose of
this is to document and observe the environment and its physical circumstances. Moreover, internet of things
(IoT) collaborated with WSN possesses enormous application; the information that is collected will be
preserved in a centralized position. Lately, the WSNs have got substantial recognition due to its applications
in various fields like military, healthcare industries and underwater observations as it is inexpensive, consumes
minimal area [1]–[3].
Lately, the WSNs have stretched their applications to various sectors like intelligent factories, and
smart cities. Here, the device, data, and network administration techniques of the WSNs play an important role.
To implement the factory operations intelligently, the sensor nodes are used for accumulating information on
machines and products. The vital part of planning a smart city is the efficient usage of the available resources
without any wastage. Hence, the WSNs are employed to provide the people and the municipal workers a well-
organized service. Figure 1 shows the typical data transmission in the WSN based environment; moreover, it
comprises source devices i.e. sensor node, cluster head also known as CH, and base station. Moreover, the data
is sensed through the source device and it is sent to the cluster head further and through the cluster head, it is
sent to a base station.

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Figure 1. Typical data transmission in WSN environment
We can observe the distribution of the sensor nodes over a maximum area as it can sense the
monitoring information and transmit the data to a server or a sink. For the successful transmission of the
accumulated data to the server, the multi-hop transmission method i employed. The server, where the data
storage takes place will be positioned far away from the source sensor node viz, out of the range of transmission.
For the determination of an ideal sink route, sensor node accumulation is done [4]–[6]. One can expect some
redundancy in the source information as the information is being accumulated by many sensors and they follow
the same phenomena though they are positioned in their respective areas. The popularly employed method in
WSN for redundancy removal and the reduction of the transmission or information size is data aggregation
(DA) viz. for the processing within the network. This method decreases the energy consumption during the
data collection. In various applications of WSN, there is no requirement to transmit the exact data that is
collected at the sensor node to the sink node and there can be processing done on the data for easy transmission.
Based on the observing purposes of applications, multiple aggregation methods can be employed for the
abstraction or compression of the raw information present in the network.
The techniques that are used here are, abstracting as {mean, variance}, maximum and minimum
values, lossy compression, feature domain reduction, and information prediction. An increase in the value of
the correlation between the information accumulated by multiple sensors increases the efficiency of the DA.
DA can successfully enhance the data accumulation’s energy efficiency where the aggregation of the sensed
information is done by the relaying nodes [7]. DA has a plethora of applications because of its dominance
inefficient energy usage. But assuring its security is a significant thing as the WSNs are mostly used in a
neglected or hostile domain where forging of the data in the process of delivery or the capturing of the sensor
nodes may be the consequences. We can say network security aims to assure confidentiality, integrity, and
availability (CIA). To achieve these multiple approaches are delineated namely, encryption, vulnerability
analysis, authentication, and detection of the attack. Yet, conventional security approaches cannot be employed
to DA directly as they can clash with a WSN with DA [8]. Considering the encryption as an example, during
the process of aggregation operations (i.e. add, multiply, subtract, divide, max/min, sum, and average) there is
a requirement of the actual plain text but the encryption of the data restricts the relay nodes from plaintext
being used.
An ideal remedy to solve this issue is the usage of a sharing key, initially, the two nodes should get
the sharing key, and then the source will encrypt its sensed information to a ciphertext and the destination gets
the ciphertext then decrypts the text using the shared key. By utilizing this method, the plaintext is transmitted
without making itself visible to other nodes [9]. Furthermore, in past, several types of research were carried
out for secure DA; the dew of them have been reviewed further. Girao et al. [10] concealed data aggregation
(CDA) was introduced; symmetric homomorphic encryption is the base for this algorithm. Here, aggregation
of the encrypted information can directly be performed by individual nodes. However, this approach has the
disadvantage of not providing proper security as every node in the CDA shares a similar key. So, if one node
is failed it troubles the whole global network. Research by Castelluccia et al. [11] delineates an algorithm that
is implemented using the one-time pad for the CDA to be enhanced. For assuring information accuracy this
algorithm needs extra data which, in turn, exceeds the communication overhead; a technique is developed
where the encryption of the information present in the network would be done using the fully homomorphic
encryption method [12]. This method will strengthen security and will decrease energy consumption. Research

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by Zhang et al. [13] presented an algorithm is presented which is efficient in DA and it utilizes techniques like
exclusive-or (XOR) homomorphic encryption and the method of probabilistic coding. Chim et al. [14]
presented by using the features of Parlier homomorphic encryption and the bloom filters. According to
Cheon et al. [15] a unique method is used where homomorphic cryptography plays an important role. By using
this method, there is no requirement for secret key management. Hence, this method removes the problem of
encrypting and decrypting the information inside the network. According to Acs and Castelluccia [16] gamma
distribution and the method of homomorphic encryption are used for introducing a new scheme. The methods
guarantee that the intruders and the aggregators cannot get the original information of each user in the process
of aggregation. According to Ding et al. [17] for the deduction of the overhead during the process of encryption
or decryption, the homomorphic encryption method is presented. This method will improve the security also
against quantum computation. Research by Kapusta et al. [18] delineates a method called as additively
homomorphic encryption and fragmentation (AHEF) scheme. Research by [19], [20] this scheme, put backs
additively homomorphic fragmentation at the place of additively homomorphic secret sharing which is
employed in the current methods. Data volume reduction and reduction in energy intake can be observed after
these changes are made in the technique, [21] and [22] also developed a secure approach for DA, which was
promising; however, they failed to address the nodes privacy [23]–[25].
In general, sensor nodes in WSN operate with limited resources like energy, storage due to the
simplicity of WSN architecture. There are several motivational areas such as energy utilization, which can
increase the network lifetime; furthermore, DA is one of the mechanisms, which helps to tackle the issue of
computation overhead, data redundancy and improvises the network lifetime. However, secure DA remains a
foremost issue in the DA; hence motivated by these, this research work designs and develops PNA-SDA
mechanism, further contribution of research work is given as: i) in this research work, the PNA-SDA
mechanism is introduced for secure and optimal DA, ii) PNA-SDA mechanism is a proximate node aware
aggregation where proximate nodes hold the information of others and further it is updated in each state, and
iii) PNA-SDA is evaluated considering average energy consumption, average deceased node; also, comparative
analysis is carried out with the existing model and PNA-SDA outperforms the existing model.
This research work comprises various distinctive section and sub-section, the first section of the
research work starts with background and application of WSN based IoT; further in the same section security
issue is addresses and different related work is reviewed. Moreover, this section ends with the motivation and
contribution of our research work. The second section involves the mathematical design of the PNA-SDA
mechanism along with the algorithm. The third section of the research work evaluates the methodology along
with comparative analysis and discussion.
2. PROPOSED METHOD
In this section, we design and develop a secure and efficient DA method. This not only preserves the
particular node privacy but also provides efficiency for network lifetime such as optimal energy consumption.
Moreover, the PNA-SDA methodologies are divided into several parts i.e. network design problem definition,
optimal, and secure DA.
2.1. Network model and problem definition
PNA-SDA methodologies initialize the network and assume that in a network nodes are self-arranged
in any given cluster; further, this network follows a hierarchical level of clustering where each node holds the
same probability of selected as cluster head as 𝑟. Let us consider any connected graph with characteristics of
undirected graph denoted as 𝐽 = (𝑋, 𝐺) which represents the node-set and communication link. The main
intention to develop the communication link is to secure the network topology information. Furthermore, any
communication link (𝑘, 𝑙) belongs to 𝐺 if and only if two distinctive edges 𝑘 and 𝑙 can communicate with one
another. Furthermore, let us consider 𝑃𝑘 as the proximate node-set, then the proximate node is mathematically
formulated as (1).
𝑃𝑘 = {𝑘|𝑙 ∈ 𝑋, (𝑘, 𝑙) ∈ 𝐺, 𝑙 𝑖𝑠 𝑛𝑜𝑡 𝑒𝑞𝑢𝑎𝑙 𝑡𝑜 1} (1)
Furthermore, let |𝑋| = 𝑝 ≥ 3 be the number of nodes in a given network and 𝑎𝑘(0) be initial node
state which is initialized as 𝑧(0) = [𝑧1(0), … . , 𝑧𝑝(0)]
𝑉
∈ Ω𝑧
0
⊆ 𝑇𝑝
.
2.2. Problem definition
To develop a secure DA mechanism, each node will communicate with the proximate node and update
the information; moreover, in order to secure, the model additional data (randomly added data similar to noise)
is added and information is sent to the proximate node. Moreover, the information sent is given through as (2).

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𝑧′(𝑚) = 𝜇𝑘(m) + 𝑧𝑘(𝑚) (2)
In (2), 𝜇𝑘 indicates the additional data with 𝑘 belongs 𝑡𝑜 𝑋; further, the updated equation can be written as (3).
𝑧𝑘(𝑚 + 1) = ∑ 𝑦𝑘𝑘𝑧𝑙(𝑚)
𝑙 + 𝑦𝑘𝑘𝑧𝑘(𝑙) 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑘 𝑏𝑒𝑙𝑜𝑛𝑔𝑠 𝑡𝑜 𝑋 (3)
The equation can be formed in terms of the matrix is written as (4).
𝑥(𝑘 + 1) = 𝑊𝑥(𝑘) (4)
The equation 𝑤𝑖𝑗 and 𝑤𝑖𝑖 are considered as the weight matrix where 𝑤𝑖𝑗 and 𝑤𝑖𝑖 is greater than 0; also 𝑊 is a
stochastic matrix where the average is computed through the (5).
lim
𝑘→∞
𝑥(𝑘) =
∑ 𝑥𝑙(0)
𝑛
𝑙=1
𝑛
= 𝑥̅ (5)
Considering the node's privacy, initial state sharing is a real concern and the node might not be willing to share
the real state to its prominent nodes; thus, we use the additional data added to the original state whenever nodes
tend to communicate with the proximate nodes. Moreover, this can be mathematically represented as (6).
𝜏 = {
𝑧′(𝑚) = 𝑧(𝑚) + 𝜇(𝑚)
𝑍(𝑚 + 1) = 𝑌𝑧
′
(𝑚)
(6)
Thus, we design an algorithm by adding the additional data such that the designed objective in the equation.
2.3. Proximate node aware secure data aggregation algorithm
The Algorithm 1 is designed for 𝑘 number of nodes. Γ indicates the threshold iteration which is equal
to 𝑟2
. Furthermore, 𝑝2
gives the guarantee of absolute DA. Also, each involved node ends the iteration once
the proximate nodes are found.
Algorithm 1. PNA-SDA algorithm
Step 1. Selecting the individual element in 𝜇𝑘(0) from random data.
Step 2. Consider 𝑧̂𝑘(0) = 𝑧𝑘(0) + 𝜇𝑘(0).
Step 3. Transmission of 𝑧̂𝑘(0) to the proximate node.
Step 4. Replace 𝜇𝑘(0)with Γ𝑘 and initialize 𝑙 = 1.
Step 5. While (𝑚 < 𝜒) do.
Update 𝑧𝑘(𝑚) considering the initialization of k and 𝑧̂𝑙(𝑚 − 1) and 𝑧̂𝑙(𝑚 − 1).
Step 6. Choose Γ𝑘(𝑚) data packets randomly.
Step 7. Compute 𝜇𝑘(0) as: 𝜇𝑘(𝑚) = Γ𝑘(𝑚) − Γ𝑘(𝑚 − 1).
Step 8. 𝑧̂𝑘(𝑚) using the (1); further, transmit the data packets to the proximate node.
𝑚 = 𝑚 + 1
End step 5 (while statement).
In the case of secure DA, node 𝑘 in the PNA-SDA model only sends the sequence i.e. 𝑧̂𝑘(𝑚) where
𝑚 = 0.1 to the proximate nodes; further, in case of each data packets of 𝑧̂𝑘(𝑚) there is additional data 𝜇𝑘(0)
which is added to 𝑧(𝑚). Thus, any external node or apart from the proximate node, other nodes will not have
any kind of information. Further, when 𝑘 is greater than or equal to 1, then 𝑧̂𝑘(𝑚) is updated and it will be
different from the initial state since each update comprises the averaging process from the information through
the proximate nodes. Thus, for all 𝑘 ∈ 𝑃𝑘, the information set for the available node 𝑖 at given iteration 𝑚 is
given as (7).
𝐾𝑘(𝑚) = {𝑧𝑘(0), 𝑧̂𝑘(0), … , 𝑧𝑘(𝑚), 𝑧̂𝑘(𝑚); 𝑧̂𝑘(𝑚) 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑙 ∈ 𝑃𝑘} (7)
In (1), packets of node 𝑖 along with proximate node output are included in:
𝐾𝑘(𝑚); also 𝐾𝑘(∞) = 𝑙𝑖𝑚𝑚→∞𝐾𝑘(𝑚).
Once the mathematical model is designed and security is analyzed, then PNA-SDA is evaluated in the next
section of the research.

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3. PERFORMANCE EVALUATION
In this section of the research, we evaluate the PNA-SDA model; moreover, the PNA-SDA model is
evaluated through designing the specific network parameter given in the Table 1. Furthermore, evaluation is
carried out on the Windows 10 platform using the visual studio 2017 integrated development environment
(IDE) using the sensoria simulator; moreover, system architecture follows the 8 GB of Cuda enabled Nvidia
RAM and 1 TB of a hard disk. Furthermore, a sensoria simulator is used for the simulation.
Table 1. Network parameter and value
Network parameter Value
Sensor nodes 50
Initial energy 0.05 joule
Compromised nodes induced 20%, 40%, and 60%
3.1. Energy consumption
Energy plays an important role in network lifetime; thus, we adopt suitable DA, which can optimize
the energy consumption. Figure 2 depicts the energy consumption considering the various percentage of
dishonest nodes. In the case of 20% dishonest nodes, the average energy consumption is 0.00712 mj, for 40%
dishonest nodes energy consumed is 0.07709 mj. Similarly, in the case of 60% of nodes, the average energy
consumption is 0.008189 mj.
Figure 2. Energy consumption
3.2. Packet identification rate
In general, considering the secured DA, the network model comprises two distinctive types of nodes
i.e. sincere nodes and compromised nodes. Thus, compromised nodes have the compromised packet. Packet
identification rate is one parameter that identifies the correct node identification. Figure 3 shows correct packet
identification comparison with the existing model through varying the percentage of compromised nodes.
Figure 3. Packet identification rate
0.007
0.0071
0.0072
0.0073
0.0074
0.0075
0.0076
0.0077
0.0078
0.0079
0.008
0.0081
0.0082
20 40 60
Energy(in
mj)
Malicious percentage of node
Average energy consumption
70
80
90
100
20 40 60
THROUGHPUT
Percentage of malicious node
PNA-SDA Packet identification_rate
Existing Proposed

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Moreover, in the case of 20% compromised nodes, the existing model achieves the packet
identification of 83 whereas the PNA-SDA model achieves an identification rate of 90. In the case of 40%
compromised nodes, the existing model identifies 86 correct packets whereas the PNA-SDA model identifies
89 packets. Similarly, for 60% of compromised nodes, existing model identifies 90 packets whereas the PNA-
SDA model identifies 96 packets.
3.3. Throughput
A general definition of throughput is the rate at which work is being done; it is one of the primary
parameters that is considered to prove the model efficiency. Figure 4 shows the throughput comparison of the
existing and PNA-SDA model by varying the number of compromised nodes (in percentage). Thus, in case of
20% nodes, throughput of existing model is 0.5395 whereas PNA-SDA model gets the throughput value of
0.585, similarly in case of 40% compromised nodes, existing model achieves the throughput of 0.3182 whereas
PNA-SDA model gets throughput of 0.3293. At last, for 60% compromised nodes, existing model gets
throughput of 0.189 and PNA-SDA model gets throughput of 0.2016.
Figure 4. Throughput comparison
3.4. Average deceased nodes
Nodes are primary components of WSN; however, number of alive nodes makes model more efficient
and it further increases the network lifetime, as less energy is required for the data transmission. Figure 5 shows
the average number of deceased node in network with various percentage of compromised nodes. In case of
20% nodes, average node deceased is 0.03. Similarly, for 40% and 60% of compromised node, deceased nodes
are 0.21.
Figure 5. Average number of deceased node in the network
0
0.1
0.2
0.3
0.4
0.5
0.6
20 40 60
THROUGHPUT
Percentage of malicious nodes
PNA-SDA throughput comparison
existing Proposed
0
0.05
0.1
0.15
0.2
0.25
20 40 60
average
deceased
node
Percentage of malicious nodes
Average deceased nodes

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3.5. Comparative analysis and discussion
In this section, we present the improvisation of PNA-SDA model over the existing model considering
the security and model efficiency as primary concern. Table 2 shows the improvisation of PNA-SDA model
over the existing model considering the packet identification. Moreover, considering other parameters like
average deceased nodes we observe that only 0.03 nodes fails on an average for 20% compromised nodes and
in case of 40% and 60% only 0.21 nodes fails to survive the network.
Table 2. Improvisation observed for correct packet identification
Percentage of compromised nodes Improvisation observed
20% 15.66%
40% 3.48%
60% 6.66%
4. CONCLUSION
WSN generates a huge range of application-based data; moreover, these data require processing and
transmission to the base station. Meanwhile, since WSNs are resource constrained, efficient data processing
and energy conservation is the primary challenge. However, these issues can be tackled through DA which
helps in avoiding redundancy and increasing the network lifetime; furthermore, security has been a major
constraint, thus this research work designed a novel mechanism named PNA-SDA which aims at secure and
efficient DA by adding the additional data and proximate node monitoring. In order to evaluate model
efficiency, average energy consumption and deceased node were considered on 20%, 40%, and 60%
compromised nodes; also, from the security, perspective packet identification parameter is evaluated along
with a comparison with an existing model. Comparative analysis indicates the improvisation of 15.66%, 3.48%,
and 6.66% of improvisation in comparison with the existing model on 20%, 40%, and 60% compromised nodes
in respective manner. Although PNA-SDA outperforms the existing model, there are other parameters like
packet misclassification, node identification need to be evaluated which would be carried out in future work.
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BIOGRAPHIES OF AUTHORS
Sushma Priyadarshini working as an assistant professor in the Computer Science
and Engineering Department of Kallerawan Charitable Trust Engineering College, Gulbarga
with experience of 19 years of teaching and a Ph.D. in the area of the wireless sensor network.
She had a workshop and faculty development program (FDP) area of interest in computer
networks and cloud computing. She can be contacted at email:
sushmapriyadarshini12@gmail.com.
Dr. Asma Parveen got graduated in electrical engineering, in 1993 and completed
post-graduation in computer science and engineering in 2004 and in 2016. She was awarded
Ph.D. in computer science and engineering. She has published many research papers in leading
international journals and conference proceedings. She can be contacted at email:
drasma.cse@gmail.com.